Quantum annealing with manufactured spins

By Mark W. Johnson, Mohammad H. Amin, Gildert Suzanne, T. Lanting, Firas Hamze, N. Dickson, R. Harris, A. Berkley, J. Johansson, P. Bunyk and 16 more. Nature, 2011

1,857 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/d0019f2ef578736cd60d7387c99c47bca4272faf

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Citations in the corpus

Quantum algorithms: an overview (2015, neutral): “, which has built large machines designed to implement this algorithm [51], with the most recent such machine (“D-Wave 2X”) announced as having up to 1152 qubits.”
A practical heuristic for finding graph minors (2014, background): “D-Wave Two solves these problems using adiabatic quantum annealing [8]: a quantum system is slowly evolved until the ground state of the system encodes the solution to the optimization problem [11, 13].”
Quantum Optimization of Fully-Connected Spin Glasses (2014, background): “[29] MW Johnson, MHS Amin, S Gildert, T Lanting, F Hamze, N Dickson, R Harris, AJ Berkley, J Johansson, P Bunyk, et al.”
Ising formulations of many NP problems (2013, methodology): “It is probable that the successful experimental implementations of AQO with the most qubits are on devices generated by DWave Systems [11, 12, 13].6 As such, we now discuss the ease with which these Hamiltonians can be encoded onto such a device.”
Strawberry Fields: A Software Platform for Photonic Quantum Computing (2018, methodology): “Popular techniques include variational quantum eigensolvers [5, 6], quantum approximate optimization algorithms [7, 8], sampling from computationally hard probability distributions [9–17], and quantum annealing [18, 19].”
Quantum Supremacy through the Quantum Approximate Optimization Algorithm (2016, methodology): “The D-Wave device [9, 16, 25, 27] is designed to run the QADI and its performance has been contrasted with simulations of the QADI as well as as with simulated annealing algorithms [8, 15, 30].”
Warm-starting quantum optimization (2020, methodology): “The quantum approximate optimization algorithm (QAOA) [16–18], inspired by Trotterization of adiabatic quantum computing [19–21], can be run on gate-based quantum computers [22, 23].”
Challenges and opportunities in quantum optimization (2023, neutral): “The problems chosen should be sufficiently hard for state-of-the-art classical solvers to leave a margin for quantum methods, but”